{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 行人数据集\n",
    "\n",
    "##  pixel_pos.csv文件格式\n",
    "\n",
    "pixel_pose.csv包含4行，第一行是所有的Frame Number；第二行是所有行人的ID；第三行是所有的y坐标；第四行是所有的x坐标。\n",
    "\n",
    "pixel_pose.csv的列数是所有行人轨迹点的数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of pedestrians is: 360, number of frames is 1448\n"
     ]
    },
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "from IPython.display import HTML\n",
    "\n",
    "# directory = \"./data/eth/hotel\"\n",
    "\n",
    "directory = \"./data/eth/univ\"\n",
    "\n",
    "file_path = os.path.join(directory, 'pixel_pos.csv')\n",
    "\n",
    "data = np.genfromtxt(file_path, delimiter=',')\n",
    "\n",
    "# Get the number of pedestrians in the current dataset\n",
    "pedIDs = np.unique(data[1, :])\n",
    "numPeds = np.size(pedIDs)\n",
    "\n",
    "pedIndexLookup = {}\n",
    "\n",
    "for ped_index, ped_id in enumerate(pedIDs):\n",
    "    pedIndexLookup[ped_id] = ped_index\n",
    "\n",
    "frameIDs = np.unique(data[0, :])\n",
    "numFrames = np.size(frameIDs)\n",
    "\n",
    "print(\"number of pedestrians is: {}, number of frames is {}\".format(numPeds, numFrames))\n",
    "\n",
    "all_frame_data = {}\n",
    "\n",
    "for frameID in frameIDs:\n",
    "    \n",
    "    traj = data[:, data[0, :] == frameID]\n",
    "    \n",
    "    all_frame_data[frameID] = traj\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(xlim=(0, 1), ylim=(0, 1))\n",
    "line, = ax.plot([], [], lw=2)\n",
    "time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes)\n",
    " \n",
    "plotnums = numPeds\n",
    "plotcols = [np.random.rand(3,) for _ in range(plotnums)]\n",
    "\n",
    "lines = []\n",
    "\n",
    "for index in range(plotnums):\n",
    "    lobj = ax.plot([],[],lw=2,color=plotcols[index], marker='o', markerfacecolor=plotcols[index],markersize = 10)[0]\n",
    "    lines.append(lobj)\n",
    " \n",
    "def init():\n",
    "    for line in lines:\n",
    "        line.set_data([],[])\n",
    "        \n",
    "    time_text.set_text('')\n",
    "    \n",
    "    return line, time_text\n",
    "\n",
    "def animate(i):\n",
    "    frame_cnt = 0\n",
    "    frame_index = i\n",
    "\n",
    "    ped_with_pos_x = {}\n",
    "    ped_with_pos_y = {}\n",
    "    while frame_cnt < 1:\n",
    "        frame_index += frame_cnt\n",
    "        if frame_index >= numFrames:\n",
    "            break\n",
    "\n",
    "        frame_id = frameIDs[frame_index]\n",
    "\n",
    "        frame_data = all_frame_data[frame_id]\n",
    "\n",
    "        for ped_index in range(len(frame_data[1])):\n",
    "            ped = frame_data[1][ped_index]\n",
    "            if not ped in ped_with_pos_x.keys():\n",
    "                ped_with_pos_x[ped] = []\n",
    "                ped_with_pos_y[ped] = []\n",
    "\n",
    "            ped_with_pos_x[ped].append(frame_data[3][ped_index])\n",
    "            ped_with_pos_y[ped].append(frame_data[2][ped_index])\n",
    "\n",
    "        frame_cnt += 1\n",
    "\n",
    "    for ped_id, positions in ped_with_pos_x.items():\n",
    "        ped_draw_index = pedIndexLookup[ped_id]\n",
    "\n",
    "        \n",
    "        if ped_draw_index < len(lines):\n",
    "            lines[ped_draw_index].set_data(list(ped_with_pos_x[ped_id]), \n",
    "                                          list(ped_with_pos_y[ped_id]))\n",
    "    \n",
    "    time_text.set_text(\"current frame:{}, total frame:{}\".format(frame_index ,numFrames))\n",
    "\n",
    "    return line, time_text\n",
    " \n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=200, interval=1000, blit=True)\n",
    " \n",
    "\n",
    "HTML(anim.to_html5_video())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(args):\n",
    "    output_size = 5\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(args.embedding_size, activation = tf.keras.activations.relu,\n",
    "            batch_input_shape = [args.batch_size, None, 2]),\n",
    "        tf.keras.layers.GRU(args.rnn_size,\n",
    "                            return_sequences=True,\n",
    "                            stateful=True,\n",
    "                            recurrent_initializer='glorot_uniform'),\n",
    "        tf.keras.layers.Dense(output_size)\n",
    "    ])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import kde\n",
    "from scipy.stats import multivariate_normal\n",
    "\n",
    "def draw_heatmap(mux, muy, sx, sy, rho, bound = 1.0):\n",
    "    x, y = np.meshgrid(np.linspace(mux - bound, mux + bound, 200),\n",
    "                       np.linspace(muy - bound, muy + bound, 200))\n",
    "    \n",
    "    mean = [mux, muy]\n",
    "\n",
    "    # Extract covariance matrix\n",
    "    cov = [[sx * sx, rho * sx * sy], [rho * sx * sy, sy * sy]]\n",
    "    \n",
    "    gaussian = multivariate_normal(mean = mean, cov = cov)\n",
    "    d = np.dstack([x, y])\n",
    "    z = gaussian.pdf(d)\n",
    "\n",
    "    z_min, z_max = -np.abs(z).max(), np.abs(z).max()\n",
    "\n",
    "    fig,ax = plt.subplots()\n",
    "\n",
    "    ax.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)\n",
    "    # ax.set_title('pcolormesh')\n",
    "    \n",
    "    # ax.axis([x.min(), x.max(), y.min(), y.max()])\n",
    "    #fig.colorbar(c, ax=ax)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "draw_heatmap(1.0, 2.0, 0.2, 0.4, 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
